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ResNetModel.py
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145 lines (113 loc) · 5.45 KB
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import sys
import tensorflow as tf
from keras.initializers import glorot_uniform
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
from keras.layers import *
from keras.models import *
from keras.optimizers import *
import json
from utils import *
with open('C:\\Users\\brain\\PycharmProjects\\Alpha-Zero-Neural-Network\\config.json') as json_data_file:
config = json.load(json_data_file)
sys.path.append('..')
def identity_block(X, f, filters, stage, block):
conv_name_base = "res" + str(stage) + block + "_branch"
bn_name_base = "bn" + str(stage) + block + "_branch"
F1, F2, F3 = filters
X_shortcut = X
X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + "2a",
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + "2a")(X)
X = LeakyReLU(alpha=0.01)(X)
X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base + "2b",
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + "2b")(X)
X = LeakyReLU(alpha=0.01)(X)
X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + "2c",
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + "2c")(X)
X = LeakyReLU(alpha=0.01)(X)
X = Add()([X, X_shortcut])
X = LeakyReLU(alpha=0.01)(X)
return X
def convolutional_block(X, f, filters, stage, block, s=2):
conv_name_base = "res" + str(stage) + block + "_branch"
bn_name_base = "bn" + str(stage) + block + "_branch"
F1, F2, F3 = filters
X_shortcut = X
X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base + "2a",
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + "2a")(X)
X = LeakyReLU(alpha=0.01)(X)
X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base + "2b",
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + "2b")(X)
X = LeakyReLU(alpha=0.01)(X)
X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + "2c",
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + "2c")(X)
X = LeakyReLU(alpha=0.01)(X)
X_shortcut = Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base + "1",
kernel_initializer=glorot_uniform(seed=0))(X_shortcut)
X_shortcut = BatchNormalization(axis=3, name=bn_name_base + "1")(X_shortcut)
X = Add()([X, X_shortcut])
X = LeakyReLU(alpha=0.01)(X)
return X
def ResNet(boardSize, args):
# Define the input as a tensor with shape input_shape
board_x, board_y = (boardSize, boardSize)
action_size = boardSize * boardSize
input_boards = Input(shape=(board_x, board_y))
X_input = Reshape((board_x, board_y, 1))(input_boards)
# Zero-Padding
X = ZeroPadding2D((3, 3))(X_input)
# Stage 1
X = Conv2D(64, (7, 7), strides=(2, 2), name='conv1', kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name='bn_conv1')(X)
X = Activation('relu')(X)
X = MaxPooling2D((3, 3), strides=(2, 2))(X)
# Stage 2
X = convolutional_block(X, f=3, filters=[64, 64, 256], stage=2, block='a', s=1)
X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')
X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')
X = identity_block(X, 3, [64, 64, 256], stage=2, block='d')
# Stage 3
X = convolutional_block(X, f=3, filters=[128, 128, 512], stage=3, block='a', s=2)
X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')
X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')
X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')
# Stage 4
X = convolutional_block(X, f=3, filters=[256, 256, 1024], stage=4, block='a', s=2)
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')
# AVGPOOL
X = AveragePooling2D(pool_size=(2, 2), padding='same')(X)
# Output layer
X = Flatten()(X)
pi = Dense(action_size, activation='linear', name='pi', kernel_initializer=glorot_uniform(seed=0))(
X) # batch_size x self.action_size
v = Dense(1, activation='tanh', name='v', kernel_initializer=glorot_uniform(seed=0))(X) # batch_size x 1
# Create model
model = Model(inputs=input_boards, outputs=[pi, v], name="ResNet")
model.compile(loss=['mean_squared_error', 'mean_squared_error'], optimizer=Adam(args.lr))
return model
def save(model, boardSize):
filename = input("Enter File Name:")
model.save(config["modelFolder"] + config["game"] + "/" + str(filename) + "." + str(boardSize) + ".h5")
print("Saved model to disk")
args = dotdict({
'lr': 0.001,
'dropout': 0.3,
'epochs': 100,
'batch_size': 94,
'cuda': True,
'num_channels': 512,
})
boardSize = config["boardSize"]
model = ResNet(boardSize, args)
save(model, boardSize)